The genetic and environmental effects on school grades in late childhood and adolescence


Autoři: Eike Friederike Eifler aff001;  Alexandra Starr aff001;  Rainer Riemann aff001
Působiště autorů: Department of Psychology, Bielefeld University, Bielefeld, Germany aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225946

Souhrn

As academic achievement can have a major impact on the development of social inequalities we set out to explore how performance differences arise. Using data of the German twin study TwinLife, genetic and environmental effects on school grades in mathematics, German and the grade point average in two age cohorts (11 and 17 years old) were identified. Structural equation modelling on the data of 432 monozygotic and 529 dizygotic twin pairs as well as 317 siblings of the twins showed substantial genetic effects (up to 62%) in both cohorts on all three variables. Next to genetic influences, the twin-specific environment as well as non-shared environmental influences were found to explain the interindividual differences in mathematics and German as well as the grade point average. A cohort effect showing itself in higher heritability in the older cohort was found for mathematics and the grade point average but not for German. Moreover, we compared twins who were assigned to the same classroom to those twins who were assigned to different classrooms and found lower effects of the twin-specific shared environment in the latter group. Our study thereby contributes to the understanding of the etiology of interindividual differences in academic achievement in the numeracy and literacy domain in two age cohorts.

Klíčová slova:

Age groups – Etiology – Heredity – Literacy – Numeracy – Schools – Teachers – Twins


Zdroje

1. Frojd SA, Nissinen ES, Pelkonen MUI, Marttunen MJ, Koivisto AM, Kaltiala Heino R. Depression and school performance in middle adolescent boys and girls. Journal of Adolescence 2008; 31(4): 485–498. Available from: URL: http://dx.doi.org/10.1016/j.adolescence.2007.08.006.

2. Nagel G, Peter R, Braig S, Hermann S, Rohrmann S, Linseisen J. The impact of education on risk factors and the occurrence of multimorbidity in the EPIC-Heidelberg cohort. BMC Public Health 2008; 8:384. doi: 10.1186/1471-2458-8-384 19014444

3. Strenze T. Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence 2007; 35(5): 401–426.

4. Krapohl E, Rimfeld K, Shakeshaft NG, Trzaskowski M, McMillan A, Pingault J-B et al. The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proc Natl Acad Sci USA 2014; 111(42): 15273–15278. doi: 10.1073/pnas.1408777111 25288728

5. Lu L, Weber HS, Spinath FM, Shi J. Predicting school achievement from cognitive and non-cognitive variables in a Chinese sample of elementary school children. Intelligence 2011; 39(2–3): 130–140. Available from: URL: http://dx.doi.org/10.1016/j.intell.2011.02.002.

6. Kriegbaum K, Becker N, Spinath B. The relative importance of intelligence and motivation as predictors of school achievement: A meta-analysis. Educational Research Review 2018; 25: 120–148.

7. Barton K, Dielman TE, Cattell RB. Personality, motivation and IQ measures as predictors of school achievement and grades: A nontechnical synopsis. Psychology in the Schools 1972; 9(1): 47–51. Available from: URL: https://doi.org/10.1002/1520-6807(197201)9:1<47::AID-PITS2310090110>3.0.CO;2-S.

8. Cucina JM, Peyton ST, Su C, Byle KA. Role of mental abilities and mental tests in explaining high-school grades. Intelligence 2016; 54: 90–104.

9. Niepel C, Brunner M, Preckel F. Achievement goals, academic self-concept, and school grades in mathematics: Longitudinal reciprocal relations in above average ability secondary school students. Contemporary Educational Psychology 2014; 39(4): 301–313.

10. Roskam I, Nils F. Predicting Intra-Individual Academic Achievement Trajectories of Adolescents Nested in Class Environment: Influence of motivation, implicit theory of intelligence, self-esteem and parenting. Psychol Belg 2013; 47(1): 119–143.

11. Stankov L. Noncognitive predictors of intelligence and academic achievement: An important role of confidence. Personality and Individual Differences 2013; 55(7): 727–732.

12. Steinmayr R, Spinath B. The importance of motivation as a predictor of school achievement. Learning and Individual Differences 2009; 19(1): 80–90.

13. Back LT, Polk E, Keys CB, McMahon SD. Classroom management, school staff relations, school climate, and academic achievement: testing a model with urban high schools. Learning Environ Res 2016; 19(3): 397–410.

14. Johnson W, McGue M, Iacono WG. Socioeconomic Status and School Grades: Placing their Association in Broader Context in a Sample of Biological and Adoptive Families. Intelligence 2007; 35(6): 526–541. doi: 10.1016/j.intell.2006.09.006 19081832

15. Steinmayr R, Heyder A, Naumburg C, Michels J, Wirthwein L. School-Related and Individual Predictors of Subjective Well-Being and Academic Achievement. Front Psychol 2018; 9: 2631. doi: 10.3389/fpsyg.2018.02631 30622497

16. Blokland GAM, McMahon KL, Thompson PM, Martin NG, de Zubicaray GI, Wright MJ. Heritability of working memory brain activation. J Neurosci 2011; 31(30): 10882–10890. doi: 10.1523/JNEUROSCI.5334-10.2011 21795540

17. Haworth CMA, Wright MJ, Luciano M, MARTIN NG, de Geus EJC, van Beijsterveldt CEM et al. The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry 2010; 15(11): 1112–1120. doi: 10.1038/mp.2009.55 19488046

18. Hofer SM, Clouston S. Commentary: On the Importance of Early Life Cognitive Abilities in Shaping Later Life Outcomes. Res Hum Dev 2014; 11(3): 241–246. doi: 10.1080/15427609.2014.936173 25309140

19. Kovas Y, Garon-Carrier G, Boivin M, Petrill SA, Plomin R, Malykh SB et al. Why children differ in motivation to learn: Insights from over 13,000 twins from 6 countries. Personality and Individual Differences 2015; 80: 51–63. doi: 10.1016/j.paid.2015.02.006 26052174

20. McGue M, Hirsch B, Lykken DT. Age and the self-perception of ability: A twin study analysis. Psychology and Aging 1993; 8(1): 72–80. Available from: URL: http://dx.doi.org/10.1037/0882-7974.8.1.72 8461118

21. Knopik VS, Neiderhiser JM, DeFries JC, Plomin R. Behavioral genetics. Seventh edition. New York: Worth Publishers; 2017.

22. Johnson W, McGue M, Iacono WG. Genetic and environmental influences on academic achievement trajectories during adolescence. Dev Psychol 2006; 42(3): 514–532. doi: 10.1037/0012-1649.42.3.514 16756442

23. Nielsen F. Achievement and Ascription in Educational Attainment: Genetic and Environmental Influences on Adolescent Schooling. Social Forces 2006; 85(1): 193–216.

24. Bartels M, Rietveld MJH, van Baal GCM, Boomsma DI. Heritability of Educational Achievement in 12-year-olds and the Overlap with Cognitive Ability. Twin res. 2002; 5(06): 544–553.

25. Wainwright MA, Wright MJ, Geffen GM, Luciano M, Martin NG. The genetic basis of academic achievement on the Queensland Core Skills Test and its shared genetic variance with IQ. Behav Genet 2005; 35(2): 133–145. doi: 10.1007/s10519-004-1014-9 15685427

26. de Zeeuw EL, de Geus EJC, Boomsma DI. Meta-analysis of twin studies highlights the importance of genetic variation in primary school educational achievement. Trends in Neuroscience and Education 2015; 4(3): 69–76.

27. Morris TT, Davies NM, Dorling D, Richmond RC, Smith GD. Testing the validity of value-added measures of educational progress with genetic data. Br Educ Res J 2018; 44(5): 725–747. doi: 10.1002/berj.3466 30983649

28. Hart SA, Petrill SA, DeThorne LS, Deater-Deckard K, Thompson LA, Schatschneider C et al. Environmental influences on the longitudinal covariance of expressive vocabulary: measuring the home literacy environment in a genetically sensitive design. J Child Psychol Psychiatry 2009; 50(8): 911–919. doi: 10.1111/j.1469-7610.2009.02074.x 19298476

29. Spinath FM, Wolf H. CoSMoS and TwinPaW: Initial Report on Two New German Twin Studies. Twin Research and Human Genetics 2006; 9(06): 787–790.

30. Gottschling J, Spengler M, Spinath B, Spinath FM. The prediction of school achievement from a behavior genetic perspective: Results from the German twin study on Cognitive Ability, Self-Reported Motivation, and School Achievement (CoSMoS). Personality and Individual Differences 2012; 53(4): 381–386.

31. de Zeeuw EL, van Beijsterveldt CEM, Glasner TJ, de Geus EJC, Boomsma DI. Arithmetic, reading and writing performance has a strong genetic component: A study in primary school children. Learning and Individual Differences 2016; 47: 156–166. doi: 10.1016/j.lindif.2016.01.009 27182184

32. Grasby KL, Coventry WL, Byrne B, Olson RK, Medland SE. Genetic and Environmental Influences on Literacy and Numeracy Performance in Australian School Children in Grades 3, 5, 7, and 9. Behav Genet 2016; 46(5): 627–648. doi: 10.1007/s10519-016-9797-z 27276978

33. Coventry WL, Byrne B, Coleman M, Olson RK, Corley R, Willcutt E et al. Does Classroom Separation Affect Twins’ Reading Ability in the Early Years of School? Twin Research and Human Genetics 2009; 12(5): 455–461. doi: 10.1375/twin.12.5.455 19803773

34. Polderman TJC, van Bartels M, Verhulst FC, Huizink AC, van Beijsterveldt CEM, Boomsma DI. No effect of classroom sharing on educational achievement in twins: a prospective, longitudinal cohort study. Journal of Epidemiology and Community Health 2010; 64(1): 36–40. doi: 10.1136/jech.2009.091629 20007633

35. Byrne B, Coventry WL, Olson RK, Wadsworth SJ, Samuelsson S, Petrill SA et al. “Teacher Effects” in Early Literacy Development: Evidence from a Study of Twins. Journal of Educational Psychology 2010; 102(1): 32–42. doi: 10.1037/a0017288 20204169

36. Tully LA, Moffitt TE, Caspi A, Taylor A, Kiernan H, Andreou P. What effect does classroom separation have on twins’ behavior, progress at school, and reading abilities? Twin res. 2004; 7(2): 115–124. doi: 10.1375/136905204323016087 15169595

37. Webbink D, Hay D, Visscher PM. Does sharing the same class in school improve cognitive abilities of twins? Twin Research and Human Genetics 2007; 10: 573–580. Available from: URL: http://dx.doi.org/10.1375/twin.10.4.573 17708698

38. White EK, Garon-Carrier G, Tosto MG, Malykh SB, Li X, Kiddle B et al. Twin classroom dilemma: To study together or separately? Dev Psychol 2018; 54(7): 1244–1254. doi: 10.1037/dev0000519 29658740

39. Kuncel NR, Crede M, Thomas LL. The Validity of Self-Reported Grade Point Averages, Class Ranks, and Test Scores: A Meta-Analysis and Review of the Literature. Review of Educational Research 2005; 75(1): 63–82. Available from: URL: http://dx.doi.org/10.3102/00346543075001063.

40. Bleidorn W, Hufer A, Kandler C, Hopwood CJ, Riemann R. A Nuclear Twin Family Study of Self-Esteem. Eur. J. Pers. 2018; 32(3): 221–232.

41. Keller MC, Medland SE, Duncan LE. Are extended twin family designs worth the trouble? A comparison of the bias, precision, and accuracy of parameters estimated in four twin family models. Behav Genet 2010; 30(3): 377–393. Available from: URL: http://dx.doi.org/10.1007/s10519-009-9320-x.

42. Hahn E, Gottschling J, Bleidorn W, Kandler C, Spengler M, Kornadt AE et al. What drives the development of social inequality over the life course? The German TwinLife Study. Twin Research and Human Genetics 2016; 19(6): 659–672. Available from: URL: http://dx.doi.org/10.1017/thg.2016.76 27748230

43. Lenau F, Hahn E. Documentation TwinLife Data: Zygosity 2017. Retrieved from https://pub.uni-bielefeld.de/download/2909616/2909932.

44. Mattheus S., Starr A., Kornadt A, Riemann R. Documentation TwinLife Data: Report Cards; 2017. https://pub.uni-bielefeld.de/download/2912025/2912026.

45. McGue M, Bouchard TJ. Adjustment of twin data for the effects of age and sex. Behav Genet 1984; 14(4): 325–343. Available from: URL: http://dx.doi.org/10.1007/BF01080045 6542356

46. Arbuckle, J. L. Amos (Version 23.0) [Computer Program] 2014.

47. Little RJA, Rubin DB. Statistical analysis with missing data. Second edition. Hoboken: John Wiley & Sons; 2014.

48. Neale MC, Cardon LR. Methodology for genetic studies of twins and families. Springer Science & Business Media; 1992.

49. Bentler PM. Comparative fit indexes in structural models. Psychological Bulletin Mar, 1990; 107(2): 238–246. Available from: URL: http://content.ebscohost.com/ContentServer.asp?T=P&P=AN&K=1990-13755-001&EbscoContent=dGJyMNLe80SeqK84y9fwOLCmr1GeprRSrq%2B4SreWxWXS&ContentCustomer=dGJyMODf44Tj3OOE4rHreefkuZKxr7NI&D=pdh 2320703

50. Browne MW, Cudeck R. Alternative Ways of Assessing Model Fit. Sociological Methods & Research 1992; 21(2): 230–258.

51. Akaike H. Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics 1969; 21(2): 243–247.

52. Akaike H. Statistical predictor identification. Annals of the Institute of Statistical Mathematics 1970; 22(2): 203–217.

53. Peng P, Wang T, Wang C, Lin X. A meta-analysis on the relation between fluid intelligence and reading/mathematics: Effects of tasks, age, and social economics status. Psychological Bulletin Feb, 2019; 145(2): 189–236. Available from: URL: http://content.ebscohost.com/ContentServer.asp?T=P&P=AN&K=2019-01878-003&EbscoContent=dGJyMNLe80Sep7Y4v%2BbwOLCmr1GeprNSrqi4SLSWxWXS&ContentCustomer=dGJyMODf44Tj3OOE4rHreefkuZKxr7NI&D=pdh.

54. Cobb-Clark DA, Salamanca N, Zhu A. Parenting style as an investment in human development. Journal of Population Economics 2019; 32(4): 1315–1352.

55. Sirin SR. Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research 2005; 75(3): 417–453. Available from: URL: http://dx.doi.org/10.3102/00346543075003417.

56. Plomin R, Defries JC, Loehlin JC. Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin Mar, 1977; 84(2): 309–322. Available from: URL: http://content.ebscohost.com/ContentServer.asp?T=P&P=AN&K=1977-24913-001&EbscoContent=dGJyMNLe80SeqK84y9fwOLCmr1GeprRSrqi4SLaWxWXS&ContentCustomer=dGJyMODf44Tj3OOE4rHreefkuZKxr7NI&D=pdh. 557211

57. von Stumm S, Plomin R. Monozygotic twin differences in school performance are stable and systematic. Dev Sci 2018; 21(6): e12694. doi: 10.1111/desc.12694 29920866


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2019 Číslo 12